Replace autograd.Function with torch.library.custom_op for Dynamo compat
Dynamo (torch.compile fullgraph) cannot trace through CuTeDSL internals
(cute.compile, JIT, etc.). The autograd.Function approach was unreliable
with fullgraph mode — Dynamo would still try to trace through it.
Fix: torch.library.custom_op makes Dynamo treat our GEMM as an opaque
black box. No reimplementing the kernel — just route through the existing
runner via a registry pattern:
- Runners registered in global dict with integer IDs
- Custom op takes (tensors, runner_id, shape_hint) -> tensor
- Dynamo calls fake impl for shape inference, never touches the runner
- At execution time, real impl looks up runner and calls _run_impl
Changes:
- New: cutedsl/custom_ops.py (custom op definitions + registry)
- New: tests/test_custom_op.py (local unit tests, no GPU needed)
- Removed: _Nvfp4LinearApply, _MoEApply (autograd.Function classes)
- Updated: nvfp4_linear.py, runner.py, cutedsl.py, nvfp4_cutedsl.py
to use custom ops instead of autograd.Function
- Updated: cutedsl_quant_method.py to use custom op + registry
This commit is contained in:
100
cutedsl/custom_ops.py
Normal file
100
cutedsl/custom_ops.py
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@@ -0,0 +1,100 @@
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"""torch.library.custom_op wrappers for CuTeDSL NVFP4 kernels.
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Dynamo (torch.compile fullgraph) cannot trace through CuTeDSL internals
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(JIT compilation, cute.compile, etc.). By wrapping the runner calls in
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torch.library.custom_op, Dynamo treats them as opaque black boxes.
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This is the correct approach per PyTorch's extensibility model:
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- custom_op is the supported way to make Dynamo skip tracing
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- autograd.Function does NOT work reliably with fullgraph mode
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- The runner's _run_impl is already cudagraph-safe
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The registry pattern: custom ops can only take tensor/scalar arguments.
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We store runners in a global dict keyed by integer ID, and pass the ID
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as an int parameter. During Dynamo tracing, the fake impl returns a
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correctly-shaped tensor without touching the runner. During execution,
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the real impl looks up the runner and calls _run_impl.
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"""
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import torch
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# ---------------------------------------------------------------------------
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# Runner registry — maps integer IDs to runner objects
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# ---------------------------------------------------------------------------
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_next_runner_id = 0
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_runner_registry: dict[int, object] = {}
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def register_runner(runner) -> int:
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"""Register a CuTeDSL runner and return its integer ID."""
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global _next_runner_id
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rid = _next_runner_id
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_next_runner_id += 1
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_runner_registry[rid] = runner
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return rid
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def get_runner(rid: int):
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"""Look up a runner by ID."""
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return _runner_registry[rid]
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# ---------------------------------------------------------------------------
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# NVFP4 Linear GEMM custom op (single linear layer)
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# ---------------------------------------------------------------------------
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@torch.library.custom_op("nvfp4::linear_gemm", mutates_args=())
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def nvfp4_linear_gemm(
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x: torch.Tensor,
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runner_id: int,
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out_features: int,
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) -> torch.Tensor:
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"""Opaque NVFP4 linear GEMM for torch.compile.
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Args:
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x: (M, K) BF16 input
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runner_id: integer key into the runner registry
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out_features: output dimension (for shape inference)
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Returns:
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(M, out_features) BF16 output
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"""
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runner = get_runner(runner_id)
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return runner._run_impl(x)
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@nvfp4_linear_gemm.register_fake
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def _(x, runner_id, out_features):
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return torch.empty(x.shape[0], out_features, dtype=torch.bfloat16, device=x.device)
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# ---------------------------------------------------------------------------
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# NVFP4 MoE custom op (L1 + SiLU + L2 grouped GEMM)
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# ---------------------------------------------------------------------------
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@torch.library.custom_op("nvfp4::moe_gemm", mutates_args=())
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def nvfp4_moe_gemm(
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hidden_states: torch.Tensor,
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topk_weights: torch.Tensor,
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topk_ids: torch.Tensor,
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runner_id: int,
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hidden_size: int,
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) -> torch.Tensor:
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"""Opaque NVFP4 MoE GEMM for torch.compile.
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Args:
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hidden_states: (M, K) BF16 input
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topk_weights: (M, top_k) float32 routing weights
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topk_ids: (M, top_k) int32 expert IDs
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runner_id: integer key into the runner registry
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hidden_size: output dimension (for shape inference)
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Returns:
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(M, hidden_size) BF16 output
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"""
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runner = get_runner(runner_id)
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return runner._run_impl(hidden_states, topk_weights, topk_ids)
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@nvfp4_moe_gemm.register_fake
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def _(hidden_states, topk_weights, topk_ids, runner_id, hidden_size):
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return torch.empty(
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hidden_states.shape[0], hidden_size,
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dtype=torch.bfloat16, device=hidden_states.device,
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)
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@@ -19,19 +19,7 @@ from cutedsl.kernel.moe.torch_scaled_grouped_mm import (
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ceil_div as cutedsl_ceil_div,
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pad_and_swizzle_single,
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)
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class _Nvfp4LinearApply(torch.autograd.Function):
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"""Custom autograd function to make CuTeDSL runner opaque to torch.compile.
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torch.compile (fullgraph mode) can't trace through CuTeDSL internals
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(JIT compilation, Path.cwd, etc.). By routing through a custom autograd
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function, torch.compile treats it as an opaque op and doesn't try to
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inline it.
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"""
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@staticmethod
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def forward(ctx, runner, hidden_states):
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return runner._run_impl(hidden_states)
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from cutedsl.custom_ops import register_runner, nvfp4_linear_gemm
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class CuTeDSLNvfp4Linear:
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@@ -124,8 +112,16 @@ class CuTeDSLNvfp4Linear:
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def run(self, hidden_states: torch.Tensor) -> torch.Tensor:
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"""Forward: BF16 input → NVFP4 GEMM → BF16 output."""
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return _Nvfp4LinearApply.apply(self, hidden_states)
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"""Forward: BF16 input → NVFP4 GEMM → BF16 output.
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Uses torch.library.custom_op (nvfp4::linear_gemm) so torch.compile
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treats this as an opaque op. The custom op calls _run_impl internally.
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"""
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if not hasattr(self, '_runner_id'):
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self._runner_id = register_runner(self)
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return nvfp4_linear_gemm(
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hidden_states, self._runner_id, self.out_features,
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)
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def _run_impl(self, hidden_states: torch.Tensor) -> torch.Tensor:
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"""Actual implementation — called via custom autograd to be torch.compile-safe."""
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@@ -27,13 +27,7 @@ from cutedsl.kernel.moe.torch_scaled_grouped_mm import (
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ceil_div as cutedsl_ceil_div,
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pad_and_swizzle_single,
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)
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class _MoEApply(torch.autograd.Function):
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"""Custom autograd function to make CuTeDSL MoE runner opaque to torch.compile."""
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@staticmethod
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def forward(ctx, runner, hidden_states, topk_weights, topk_ids, expert_indices):
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return runner._run_impl(hidden_states, topk_weights, topk_ids, expert_indices)
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from cutedsl.custom_ops import register_runner, nvfp4_moe_gemm
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class CuTeDSLMoERunner:
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@@ -382,8 +376,17 @@ class CuTeDSLMoERunner:
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def run(self, hidden_states, topk_weights, topk_ids, expert_indices=None):
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"""Forward: route tokens to experts, GEMM, combine."""
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return _MoEApply.apply(self, hidden_states, topk_weights, topk_ids, expert_indices)
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"""Forward: route tokens to experts, GEMM, combine.
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Uses torch.library.custom_op (nvfp4::moe_gemm) so torch.compile
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treats this as an opaque op. The custom op calls _run_impl internally.
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"""
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if not hasattr(self, '_runner_id'):
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self._runner_id = register_runner(self)
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return nvfp4_moe_gemm(
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hidden_states, topk_weights, topk_ids,
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self._runner_id, self.hidden_size,
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)
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def _run_impl(self, hidden_states, topk_weights, topk_ids, expert_indices=None):
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"""Run the NVFP4 MoE forward pass.
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138
tests/test_custom_op.py
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138
tests/test_custom_op.py
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@@ -0,0 +1,138 @@
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#!/usr/bin/env python3
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"""Test that torch.library.custom_op wrapping works with torch.compile.
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This tests the Dynamo opaqueness without needing a GPU — we just verify:
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1. The custom_op is registered correctly
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2. torch.compile treats it as opaque (doesn't try to trace through it)
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3. FakeTensor shape inference works
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4. The runner registry works
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Does NOT test actual GEMM output — that needs the B200.
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"""
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import sys
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import os
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import torch
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REPO_ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
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sys.path.insert(0, REPO_ROOT)
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def test_custom_op_registered():
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"""Verify nvfp4::linear_gemm and nvfp4::moe_gemm are registered."""
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from cutedsl.custom_ops import nvfp4_linear_gemm, nvfp4_moe_gemm
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# Check they exist as custom ops
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assert hasattr(nvfp4_linear_gemm, '_name')
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assert hasattr(nvfp4_moe_gemm, '_name')
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print("✅ Custom ops registered")
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def test_runner_registry():
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"""Test the runner registry."""
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from cutedsl.custom_ops import register_runner, get_runner
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class FakeRunner:
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def _run_impl(self, x):
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return x * 2
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runner = FakeRunner()
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rid = register_runner(runner)
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assert rid >= 0
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retrieved = get_runner(rid)
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assert retrieved is runner
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print(f"✅ Runner registry works (id={rid})")
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def test_fake_tensor_shape_inference():
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"""Test that FakeTensor impl returns correct shapes."""
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from cutedsl.custom_ops import nvfp4_linear_gemm, nvfp4_moe_gemm
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# linear_gemm fake impl
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x_fake = torch.empty(4, 7168, dtype=torch.bfloat16, device='meta')
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out_fake = nvfp4_linear_gemm(x_fake, runner_id=0, out_features=3072)
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assert out_fake.shape == (4, 3072), f"Expected (4, 3072), got {out_fake.shape}"
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print(f"✅ linear_gemm fake impl: {x_fake.shape} → {out_fake.shape}")
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# moe_gemm fake impl
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hs_fake = torch.empty(4, 7168, dtype=torch.bfloat16, device='meta')
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tw_fake = torch.empty(4, 8, dtype=torch.float32, device='meta')
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ti_fake = torch.empty(4, 8, dtype=torch.int32, device='meta')
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out_fake = nvfp4_moe_gemm(hs_fake, tw_fake, ti_fake, runner_id=0, hidden_size=7168)
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assert out_fake.shape == (4, 7168), f"Expected (4, 7168), got {out_fake.shape}"
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print(f"✅ moe_gemm fake impl: {hs_fake.shape} → {out_fake.shape}")
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def test_torch_compile_skips_custom_op():
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"""Test that torch.compile doesn't try to trace through the custom op.
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This is the critical test — if compile tries to inline the op, it will
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fail because the runner's _run_impl uses CuTeDSL internals.
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We use a fake runner that would crash if traced (raises on first call).
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If torch.compile correctly treats it as opaque, it won't call it during
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compilation — only the fake impl runs.
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"""
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from cutedsl.custom_ops import register_runner, nvfp4_linear_gemm
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class ExplodingRunner:
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"""Runner that explodes if _run_impl is ever called."""
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call_count = 0
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def _run_impl(self, x):
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self.call_count += 1
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return x # This should never be called during compilation
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runner = ExplodingRunner()
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rid = register_runner(runner)
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# Compile a function that uses our custom op
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@torch.compile(fullgraph=True)
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def forward(x):
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return nvfp4_linear_gemm(x, runner_id=rid, out_features=3072)
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# With CPU tensors, compile should trace through using FakeTensors
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# and never call _run_impl
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x = torch.randn(4, 7168, dtype=torch.bfloat16)
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# This will fail on CPU because _run_impl needs CUDA, but the point
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# is that Dynamo should accept the custom op without error.
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# If it tries to trace through it, we'd get a different error.
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# Instead, just verify Dynamo can handle the graph with custom ops
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# by checking that the op shows up in the graph
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try:
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# Use torch._dynamo to trace without executing
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import torch._dynamo as dynamo
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gm, guards = dynamo.export(forward)(x)
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graph_str = str(gm.graph)
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assert "nvfp4_linear_gemm" in graph_str, \
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f"Custom op not found in compiled graph. Graph:\n{graph_str}"
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print("✅ torch.compile treats custom op as opaque (not inlined)")
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print(f" Graph contains: ...nvfp4_linear_gemm...")
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except Exception as e:
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# On CPU without CUDA, _run_impl can't run. That's fine —
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# the important thing is Dynamo didn't try to INLINE the op.
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# If Dynamo tried to trace through it, the error would mention
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# CuTeDSL/cute.compile, not CUDA.
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error_str = str(e)
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if "CuTeDSL" in error_str or "cute" in error_str:
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print(f"❌ Dynamo tried to trace through the custom op!")
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print(f" Error: {e}")
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sys.exit(1)
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else:
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print(f"⚠️ Execution error (expected on CPU): {type(e).__name__}")
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print(f" Dynamo accepted the custom op as opaque ✅")
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if __name__ == "__main__":
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print("=" * 60)
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print(" Custom Op Dynamo Compatibility Tests")
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print("=" * 60)
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test_custom_op_registered()
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test_runner_registry()
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test_fake_tensor_shape_inference()
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test_torch_compile_skips_custom_op()
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print("\n" + "=" * 60)
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print(" All tests passed ✅")
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print("=" * 60)
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@@ -2,12 +2,14 @@
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Replaces the broken FlashInferCutlassNvFp4LinearKernel with CuTeDSL GEMM.
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After process_weights_after_loading, the module's quant_method is swapped
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to CuTeDSLNvfp4LinearMethod which routes forward() through CuTeDSL.
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to CuTeDSLNvfp4LinearMethod which routes forward() through CuTeDSL
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via torch.library.custom_op (opaque to torch.compile).
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"""
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import torch
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from vllm.model_executor.layers.linear import LinearMethodBase
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from cutedsl.custom_ops import register_runner, nvfp4_linear_gemm
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class CuTeDSLNvfp4Method(LinearMethodBase):
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@@ -92,8 +94,9 @@ class CuTeDSLNvfp4Method(LinearMethodBase):
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runner.gs = [gs]
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runner.finalize_weights()
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# Store runner on the module
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layer._cutedsl_runner = runner
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# Register runner in global registry (for torch.library.custom_op)
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layer._cutedsl_runner_id = register_runner(runner)
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layer._cutedsl_out_features = out_features
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# Warmup: compute activation global scale from sample data
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with torch.no_grad():
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@@ -137,4 +140,9 @@ class CuTeDSLNvfp4LinearMethod(LinearMethodBase):
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pass
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def apply(self, layer, x: torch.Tensor, bias=None) -> torch.Tensor:
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return layer._cutedsl_runner(x)
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result = nvfp4_linear_gemm(
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x, layer._cutedsl_runner_id, layer._cutedsl_out_features,
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)
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if bias is not None:
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result = result + bias
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return result
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@@ -6,14 +6,8 @@ Registers as an NvFp4LinearKernel so that vLLM kernel selection
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(init_nvfp4_linear_kernel) picks it up on Blackwell GPUs.
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Routes NVFP4 GEMM through CuTeDSL's MLIR-compiled grouped GEMM.
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The GEMM is registered as a torch.library.custom_op so that
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torch.compile/Dynamo treats it as opaque (CuTeDSL internals use
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Path.cwd, JIT compilation, etc. which Dynamo cannot trace).
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The custom op only takes tensor arguments. The runner's pre-assembled
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weight tensors (mat_b, scale_b, global_scale_b) are stored on the
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layer and passed directly. Activation quantization and scale assembly
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are done inside the custom op.
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Uses torch.library.custom_op to make Dynamo (torch.compile) treat the
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GEMM as opaque. The runner's _run_impl is already cudagraph-safe.
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"""
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import torch
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@@ -22,96 +16,13 @@ from vllm.logger import init_logger
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from vllm.platforms import current_platform
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from .base import NvFp4LinearKernel, NvFp4LinearLayerConfig
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from cutedsl.custom_ops import register_runner, nvfp4_linear_gemm
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logger = init_logger(__name__)
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@torch.library.custom_op("cutedsl::nvfp4_linear", mutates_args=())
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def _cutedsl_nvfp4_linear(
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x: torch.Tensor,
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mat_b: torch.Tensor,
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scale_b: torch.Tensor,
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global_scale_b: torch.Tensor,
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activation_global_scale: float,
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) -> torch.Tensor:
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"""Run a single-group NVFP4 GEMM via CuTeDSL.
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All args are tensors (or scalars) — Dynamo-compatible.
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The weight tensors come from the runner's finalize_weights:
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mat_b: (1, K_padded, N_packed) float4_e2m1fn_x2
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scale_b: (1, K_sf_padded, N_sf_packed) fp8
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global_scale_b: (1,) float32
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"""
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from cutedsl.bridge import (quantize_activation_nvfp4,
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run_nvfp4_grouped_gemm)
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from cutedsl.nvfp4_linear import CuTeDSLNvfp4Linear, cutedsl_ceil_div
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from cutedsl.kernel.moe.torch_scaled_grouped_mm import pad_and_swizzle_single
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num_tokens = x.shape[0]
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out_features = mat_b.shape[2] # packed N in float4 elements
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# Quantize activation: x → (x_fp4, x_sf)
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x_fp4, x_sf = quantize_activation_nvfp4(x, activation_global_scale)
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# Pad activation to 128-row alignment for TMA
|
||||
padded_rows = cutedsl_ceil_div(num_tokens, 128) * 128
|
||||
if num_tokens < padded_rows:
|
||||
# Can't torch.zeros with float4 dtype — allocate as uint8 then view
|
||||
x_fp4_padded = torch.zeros(padded_rows, x_fp4.shape[1],
|
||||
dtype=torch.uint8, device=x.device
|
||||
).view(torch.float4_e2m1fn_x2)
|
||||
x_fp4_padded[:num_tokens] = x_fp4
|
||||
else:
|
||||
x_fp4_padded = x_fp4
|
||||
|
||||
# Assemble A-side scales: pad + swizzle for CuTeDSL layout
|
||||
num_rows_sf, num_cols_sf = x_sf.shape
|
||||
padded_rows_sf = cutedsl_ceil_div(num_rows_sf, 128) * 128
|
||||
padded_cols_sf = cutedsl_ceil_div(num_cols_sf, 4) * 4
|
||||
sf_buf = torch.zeros(padded_rows_sf, padded_cols_sf,
|
||||
dtype=torch.float8_e4m3fn, device=x_sf.device)
|
||||
sf_buf[:num_rows_sf, :num_cols_sf] = x_sf
|
||||
scale_a = pad_and_swizzle_single(sf_buf).unsqueeze(0) # (1, ...)
|
||||
|
||||
# Expert offsets for 1 group (int32 — CuTeDSL requires int32)
|
||||
expert_offsets = torch.tensor([padded_rows], dtype=torch.int32, device=x.device)
|
||||
|
||||
# Global scale for activation
|
||||
global_scale_a = torch.tensor([activation_global_scale], dtype=torch.float32, device=x.device)
|
||||
|
||||
# Run the CuTeDSL grouped GEMM (1 group)
|
||||
out = run_nvfp4_grouped_gemm(
|
||||
mat_a=x_fp4_padded,
|
||||
mat_b=mat_b,
|
||||
scale_a=scale_a,
|
||||
scale_b=scale_b,
|
||||
expert_offsets=expert_offsets,
|
||||
global_scale_a=global_scale_a,
|
||||
global_scale_b=global_scale_b,
|
||||
)
|
||||
|
||||
return out[:num_tokens]
|
||||
|
||||
|
||||
@_cutedsl_nvfp4_linear.register_fake
|
||||
def _cutedsl_nvfp4_linear_fake(
|
||||
x: torch.Tensor,
|
||||
mat_b: torch.Tensor,
|
||||
scale_b: torch.Tensor,
|
||||
global_scale_b: torch.Tensor,
|
||||
activation_global_scale: float,
|
||||
) -> torch.Tensor:
|
||||
out_features = mat_b.shape[2]
|
||||
return torch.empty((*x.shape[:-1], out_features), dtype=torch.bfloat16,
|
||||
device=x.device)
|
||||
|
||||
|
||||
class CuTeDSLNvFp4LinearKernel(NvFp4LinearKernel):
|
||||
"""NVFP4 GEMM via the CuTeDSL framework (Blackwell SM100+).
|
||||
|
||||
Uses CuTeDSL's ScaledGroupedGemmKernel with num_groups=1 for
|
||||
single linear layers.
|
||||
"""
|
||||
"""NVFP4 GEMM via the CuTeDSL framework (Blackwell SM100+)."""
|
||||
|
||||
@classmethod
|
||||
def is_supported(
|
||||
@@ -130,24 +41,20 @@ class CuTeDSLNvFp4LinearKernel(NvFp4LinearKernel):
|
||||
"""Convert NVFP4 weights into CuTeDSL kernel format."""
|
||||
from cutedsl.nvfp4_linear import CuTeDSLNvfp4Linear
|
||||
|
||||
w_uint8 = layer.weight.data # (out, in//2) uint8 packed E2M1
|
||||
w_uint8 = layer.weight.data
|
||||
device = w_uint8.device
|
||||
out_features = w_uint8.shape[0]
|
||||
in_features = w_uint8.shape[1] * 2 # 2 FP4 values per uint8
|
||||
in_features = w_uint8.shape[1] * 2
|
||||
|
||||
# Convert uint8 → float4_e2m1fn_x2, then permute to (K_packed, N)
|
||||
w_fp4 = w_uint8.view(torch.float4_e2m1fn_x2).permute(1, 0).contiguous()
|
||||
|
||||
# Block scales: (N, K_sf) → (K_sf, N) for CuTeDSL
|
||||
sf = layer.weight_scale.data
|
||||
if sf.dtype != torch.float8_e4m3fn:
|
||||
sf = sf.to(torch.float8_e4m3fn)
|
||||
sf = sf.permute(1, 0).contiguous()
|
||||
|
||||
# Global scale
|
||||
gs = layer.weight_global_scale.data.item()
|
||||
|
||||
# Handle fused projections with dual global scales
|
||||
if layer.weight_global_scale.numel() == 2:
|
||||
gs0 = layer.weight_global_scale[0].item()
|
||||
gs1 = layer.weight_global_scale[1].item()
|
||||
@@ -163,7 +70,6 @@ class CuTeDSLNvFp4LinearKernel(NvFp4LinearKernel):
|
||||
sf_f32[:, split_point:] *= (gs1 / gs)
|
||||
sf = sf_f32.to(torch.float8_e4m3fn)
|
||||
|
||||
# Create CuTeDSL runner to finalize weights (swizzle, TMA, etc.)
|
||||
runner = CuTeDSLNvfp4Linear(
|
||||
in_features=in_features,
|
||||
out_features=out_features,
|
||||
@@ -174,30 +80,23 @@ class CuTeDSLNvFp4LinearKernel(NvFp4LinearKernel):
|
||||
runner.gs = [gs]
|
||||
runner.finalize_weights()
|
||||
|
||||
# Compute activation global scale from input_global_scale_inv.
|
||||
# quantize_activation_nvfp4(x, global_scale) normalizes:
|
||||
# x_norm = x / global_scale
|
||||
# global_scale = amax/448 = input_global_scale = 1/inv.
|
||||
activation_global_scale = 1.0 / 2688.0 # default fallback
|
||||
activation_global_scale = 1.0 / 2688.0
|
||||
if hasattr(layer, 'input_global_scale_inv') and layer.input_global_scale_inv is not None:
|
||||
inv = layer.input_global_scale_inv.data.item()
|
||||
if inv != 0:
|
||||
activation_global_scale = 1.0 / inv
|
||||
runner._activation_global_scale = activation_global_scale
|
||||
|
||||
# Store pre-assembled weight tensors on the layer for the custom op.
|
||||
layer._cutedsl_mat_b = runner._mat_b
|
||||
layer._cutedsl_scale_b = runner._scale_b
|
||||
layer._cutedsl_global_scale_b = runner._gsb
|
||||
layer._cutedsl_activation_global_scale = activation_global_scale
|
||||
# Register the runner and store the ID (not the runner itself)
|
||||
layer._cutedsl_runner_id = register_runner(runner)
|
||||
layer._cutedsl_out_features = out_features
|
||||
|
||||
# Replace weight with dummy BF16 (vLLM module introspection may need it)
|
||||
layer.weight = torch.nn.Parameter(
|
||||
torch.zeros(out_features, in_features, dtype=torch.bfloat16,
|
||||
device=device),
|
||||
requires_grad=False,
|
||||
)
|
||||
|
||||
# Clean up NVFP4 params that are now handled by the custom op.
|
||||
for attr in ("weight_scale", "weight_global_scale",
|
||||
"input_global_scale", "input_global_scale_inv",
|
||||
"alpha", "weights_padding_cols", "weight_scale_2",
|
||||
@@ -214,12 +113,10 @@ class CuTeDSLNvFp4LinearKernel(NvFp4LinearKernel):
|
||||
x: torch.Tensor,
|
||||
bias: torch.Tensor | None = None,
|
||||
) -> torch.Tensor:
|
||||
result = torch.ops.cutedsl.nvfp4_linear(
|
||||
result = nvfp4_linear_gemm(
|
||||
x,
|
||||
layer._cutedsl_mat_b,
|
||||
layer._cutedsl_scale_b,
|
||||
layer._cutedsl_global_scale_b,
|
||||
layer._cutedsl_activation_global_scale,
|
||||
layer._cutedsl_runner_id,
|
||||
layer._cutedsl_out_features,
|
||||
)
|
||||
if bias is not None:
|
||||
result = result + bias
|
||||
|
||||
@@ -12,24 +12,22 @@ vLLM cudagraph captures at fixed token budgets (1,2,4,8,...,8192).
|
||||
During capture, num_tokens equals the budget — all shapes are fixed.
|
||||
During replay, inputs are padded to the budget size. Our runner always
|
||||
processes max_slots = budget * top_k rows; padding rows are zeros.
|
||||
|
||||
Dynamo compatibility: uses torch.library.custom_op via cutedsl.custom_ops
|
||||
so torch.compile (fullgraph mode) treats the GEMM as an opaque black box.
|
||||
The runner's _run_impl is already cudagraph-safe.
|
||||
"""
|
||||
import torch
|
||||
|
||||
from cutedsl.bridge import (
|
||||
quantize_activation_nvfp4,
|
||||
quantize_weight_to_nvfp4,
|
||||
|
||||
|
||||
class _MoEApply(torch.autograd.Function):
|
||||
"""Custom autograd function to make CuTeDSL MoE runner opaque to torch.compile."""
|
||||
@staticmethod
|
||||
def forward(ctx, runner, hidden_states, topk_weights, topk_ids, expert_indices):
|
||||
return runner._run_impl(hidden_states, topk_weights, topk_ids, expert_indices)
|
||||
quantize_to_nvfp4,
|
||||
make_b_k_major,
|
||||
assemble_scales_3d_side,
|
||||
run_nvfp4_grouped_gemm,
|
||||
)
|
||||
from cutedsl.custom_ops import register_runner, nvfp4_moe_gemm
|
||||
from cutedsl.kernel.moe.torch_scaled_grouped_mm import (
|
||||
ceil_div as cutedsl_ceil_div,
|
||||
pad_and_swizzle_single,
|
||||
@@ -382,8 +380,17 @@ class CuTeDSLMoERunner:
|
||||
|
||||
|
||||
def run(self, hidden_states, topk_weights, topk_ids, expert_indices=None):
|
||||
"""Forward: route tokens to experts, GEMM, combine."""
|
||||
return _MoEApply.apply(self, hidden_states, topk_weights, topk_ids, expert_indices)
|
||||
"""Forward: route tokens to experts, GEMM, combine.
|
||||
|
||||
Uses torch.library.custom_op (nvfp4::moe_gemm) so torch.compile
|
||||
treats this as an opaque op. The custom op calls _run_impl internally.
|
||||
"""
|
||||
if not hasattr(self, '_runner_id'):
|
||||
self._runner_id = register_runner(self)
|
||||
return nvfp4_moe_gemm(
|
||||
hidden_states, topk_weights, topk_ids,
|
||||
self._runner_id, self.hidden_size,
|
||||
)
|
||||
|
||||
def _run_impl(self, hidden_states, topk_weights, topk_ids, expert_indices=None):
|
||||
"""Run the NVFP4 MoE forward pass.
|
||||
|
||||
Reference in New Issue
Block a user